63
/100

SOC 53-7081

Refuse and Recyclable Material Collectors

High RiskFrey/Osborne: 93.0%

Risk Score

โš ๏ธ

63/100

High Risk

US Employment

๐Ÿ‘ฅ

139,180

Total workers

Median Wage

๐Ÿ’ฐ

$48K

$32K โ€“ $75K

Projected Growth

๐Ÿ“ˆ

+0.9%

2023-2033 (BLS)

GenAI Exposure

๐Ÿค–

44/100

Moderate exposure

How we calculate these numbers โ†’

๐Ÿ’ก Refuse and Recyclable Material Collectors face a risk score of 63/100 โ€” 19 points above the national average of 44. With only 44/100 GenAI exposure, most core tasks remain resistant to current AI capabilities. See our methodology โ†’

๐Ÿ’ก Workers in this field earn $48K ($2K above the national median). The 3 recommended career transitions all maintain competitive wages while reducing automation exposure. Explore transition paths โ†’

๐Ÿ” AI Impact Analysis

With a risk score of 63/100, Refuse and Recyclable Material Collectors faces moderate automation pressure. While tasks like drone delivery displacing last-mile logistics workers are increasingly handled by AI, the role retains significant human elements. The 139,180 workers in this occupation should focus on strengthening skills in navigating unpredictable road and weather conditions and emergency situation response and quick decision-making to stay ahead. The role will likely evolve rather than disappear.

Will AI Replace Refuse and Recyclable Material Collectors?

Read our full analysis with verdict, risk factors, safe tasks, and career transition paths โ†’

โš ๏ธ Top Risk Factors

1

Drone delivery displacing last-mile logistics workers

2

Autonomous vehicle and self-driving truck technology

3

Automated warehouse sorting and loading systems

4

AI traffic management and fleet coordination

๐Ÿ›ก๏ธ Tasks Safe from Automation

โœ“

Navigating unpredictable road and weather conditions

โœ“

Emergency situation response and quick decision-making

โœ“

Customer interaction and conflict resolution during delivery

๐Ÿ“Š Task Automation Breakdown

Based on O*NET task analysis and GenAI exposure scoring. Shows the estimated proportion of this occupation's core tasks that are automatable by current AI, augmented by AI tools, or require essential human skills.

๐Ÿ“‹ O*NET Task Profile

โ€ข

Inspect trucks prior to beginning routes to ensure safe operating condition.

โ€ข

Drive trucks, following established routes, through residential streets or alleys or through business or industrial areas.

โ€ข

Refuel trucks or add other fluids, such as oil or brake fluid.

โ€ข

Dump refuse or recyclable materials at disposal sites.

โ€ข

Fill out defective equipment reports.

๐Ÿ’ป Technology Skills

โ€ข

Analytical or scientific software

โ€ข

Facilities management software

โ€ข

Data base user interface and query software

โ€ข

Materials requirements planning logistics and supply chain software

โ€ข

Mobile location based services software

๐ŸŽ“ Key Knowledge Areas

โ€ข

Mechanical

โ€ข

English Language

โ€ข

Transportation

โ€ข

Customer and Personal Service

โ€ข

Public Safety and Security

๐Ÿ“Š vs National Average

Median Wage$48K
+$2K

National avg: $46K

Risk Score63/100
+19

National avg: 44/100

GenAI Exposure44/100
+6

National avg: 38/100

Projected Growth0.9%
-2.8%

National avg: 3.7%

๐Ÿ”„ Career Transition Paths

OccupationRiskWageOverlap
First-Line Supervisors of Transportation and Material Moving Workers, Except Aircraft Cargo Handling Supervisors25$62K76%
Aircraft Cargo Handling Supervisors29$64K83%
Air Transportation Workers31$107K79%